{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7d7e50af",
   "metadata": {},
   "source": [
    "### Import dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eb649b04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 750 ms (started: 2024-07-29 18:58:41 +02:00)\n"
     ]
    }
   ],
   "source": [
    "%load_ext autotime\n",
    "\n",
    "from IPython.display import display_html\n",
    "from PIL import Image as PILImage\n",
    "\n",
    "from img2table.document import Image\n",
    "from img2table.ocr import TesseractOCR"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ec5c901",
   "metadata": {},
   "source": [
    "The <code>implicit_rows</code> parameter is used to split existing rows into smaller ones if:\n",
    "1. The row contains multi-line cells\n",
    "2. Vertical separation between elements of the cell is large enough\n",
    "\n",
    "The same principle is applied at the column level using the <code>implicit_columns</code> parameter."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21336bb3",
   "metadata": {},
   "source": [
    "### Image used for the example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "023b4276",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=632x344>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 31 ms (started: 2024-07-29 18:58:43 +02:00)\n"
     ]
    }
   ],
   "source": [
    "PILImage.open(\"data/implicit.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b595d562",
   "metadata": {},
   "source": [
    "### Instantiate objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a172a717",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 94 ms (started: 2024-07-29 18:58:45 +02:00)\n"
     ]
    }
   ],
   "source": [
    "# Define OCR instance, requires prior installation of Tesseract-OCR\n",
    "ocr = TesseractOCR()\n",
    "\n",
    "# Define image\n",
    "img = Image(src=\"data/implicit.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb700fe0",
   "metadata": {},
   "source": [
    "### Extract tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "494e62de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.34 s (started: 2024-07-29 18:58:46 +02:00)\n"
     ]
    }
   ],
   "source": [
    "# Extract tables without implicit rows\n",
    "extracted_tables = img.extract_tables(ocr=ocr, implicit_rows=False, implicit_columns=False)\n",
    "table = extracted_tables.pop()\n",
    "\n",
    "# Extract tables with implicit rows\n",
    "extracted_tables_implicit = img.extract_tables(ocr=ocr, implicit_rows=True, implicit_columns=False)\n",
    "table_implicit_rows = extracted_tables_implicit.pop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0253eb9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3 style=\"text-align: center\">Regular table</h3>\n",
       "                   <p style=\"text-align: center\">\n",
       "                       <b>Title:</b> No title detected<br>\n",
       "                       <b>Bounding box:</b> x1=64, y1=13, x2=562, y2=315\n",
       "                   </p>\n",
       "                   <div align=\"center\"><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Col1</td>\n",
       "      <td>Col 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Row 1</td>\n",
       "      <td>Value 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Implicit row 1\\nImplicit row 2\\nImplicit row 3</td>\n",
       "      <td>Implicit value 1\\nImplicit value 2\\nImplicit value 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Row 3</td>\n",
       "      <td>Value 3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>\n",
       "                   <hr>\n",
       "                "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h3 style=\"text-align: center\">Table with implicit rows</h3>\n",
       "                   <p style=\"text-align: center\">\n",
       "                       <b>Title:</b> No title detected<br>\n",
       "                       <b>Bounding box:</b> x1=64, y1=13, x2=562, y2=315\n",
       "                   </p>\n",
       "                   <div align=\"center\"><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Col1</td>\n",
       "      <td>Col 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Row 1</td>\n",
       "      <td>Value 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Implicit row 1</td>\n",
       "      <td>Implicit value 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Implicit row 2</td>\n",
       "      <td>Implicit value 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Implicit row 3</td>\n",
       "      <td>Implicit value 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Row 3</td>\n",
       "      <td>Value 3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>\n",
       "                   <hr>\n",
       "                "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 16 ms (started: 2024-07-29 18:58:51 +02:00)\n"
     ]
    }
   ],
   "source": [
    "display_html(table.html_repr(title=\"Regular table\"), raw=True)\n",
    "display_html(table_implicit_rows.html_repr(title=\"Table with implicit rows\"), raw=True)"
   ]
  }
 ],
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